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拖動你的GAN:在生成圖像歧管…
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Synthesizing visual content that meets users’ needs often requires flexible and precise controllability of the pose， shape， expression， and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model， which often lack flexibility， precision， and generality. In this work， we study a powerful yet much less explored way of controlling GANs， that is， to

拖動你的GAN:在生成圖像歧管上的交互式基于點的操作

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